Fig. 1. Maximum pooling example

Fig. 2. Classification process for j^th category

Fig. 3. One-hand and double-hand gesture recognition structure based on two classifiers

Fig. 4. Network structure of hand number classifier

Fig. 5. Calculation of distance between centers of gavity of hand gestures

Fig. 6. Diagrams of gesture grouping prediction. (a) Gesture binary graphs; (b) centers of gravity of hand gestures; (c) gesture grouping prediction maps

Fig. 7. Adaptive enhanced convolutional neural network structure

Fig. 8. Nine types of gesture samples from ASL

Fig. 9. Samples of one-hand and double-hand gesture data sets. (a) One-hand gestures; (b) double-hand gestures

Fig. 10. Data expansion and complex background gesture samples. (a) Complex background gestures; (b) data expansion

Fig. 11. Convergence and error rate curves of CNN and AE-CNN. (a) Convergence curves of CNN, CNN+Dropout,and AE-CNN; (b) error rate curves of CNN and AE-CNN

Fig. 12. LBP features of hand gestures (0,2,5, and 9). (a) LBP feature of zero gesture; (b) LBP feature of two gesture; (c) LBP feature of five gesture; (d) LBP feature of nine gesture

Fig. 13. HOG features of partial gestures and HOG+PCA dimensionality reduction reconstruction maps

Fig. 14. Preprocessing graphs after adding different noise. (a) Normalization of salt and pepper noise; (b) binary map of salt and pepper noise; (c) binary map of Gaussian noise; (d) distribution of Gaussian noise density; (e) normalization of Gaussian noise